Real Time Data Pipeline Engineering for Scalable Insights
Padmaja Pulivarthy, Mohanarajesh Kommineni, Venu Madhav Aragani, G. Rajassekaran · 2025
The exponential growth of data in modern digital environments demands efficient real-time processing capabilities to provide timely and actionable insights. This research focuses on developing a robust real-time data pipeline that handles high-velocity, high-volume data, ensuring scalability, low-latency processing, and fault tolerance. The pipeline demonstrated the ability to ingest data at 1 million events per second, maintaining an average latency between 10 to 15 milliseconds. Scalability tests revealed that the system could effectively handle increasing data loads by dynamically adjusting resources, reducing processing time by up to 30% when additional nodes were added. Furthermore, the pipeline's fault tolerance mechanisms, including data replication and automated recovery, ensured over 99.9% reliability, preserving data integrity even during node failures. Visualizations, such as heatmaps and impedance graphs, highlighted the pipeline's efficiency in managing data flow across various stages, from ingestion to analytics.